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AWS Certified Professional AWS Certified Generative AI Developer-Professional

AWS Certified Generative AI Developer-Professional

Last Update Feb 3, 2026
Total Questions : 85

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Questions 2

A company is implementing a serverless inference API by using AWS Lambda. The API will dynamically invoke multiple AI models hosted on Amazon Bedrock. The company needs to design a solution that can switch between model providers without modifying or redeploying Lambda code in real time. The design must include safe rollout of configuration changes and validation and rollback capabilities.

Which solution will meet these requirements?

Options:

A.  

Store the active model provider in AWS Systems Manager Parameter Store. Configure a Lambda function to read the parameter at runtime to determine which model to invoke.

B.  

Store the active model provider in AWS AppConfig. Configure a Lambda function to read the configuration at runtime to determine which model to invoke.

C.  

Configure an Amazon API Gateway REST API to route requests to separate Lambda functions. Hardcode each Lambda function to a specific model provider. Switch the integration target manually.

D.  

Store the active model provider in a JSON file hosted on Amazon S3. Use AWS AppConfig to reference the S3 file as a hosted configuration source. Configure a Lambda function to read the file through AppConfig at runtime to determine which model to invoke.

Discussion 0
Questions 3

A company is developing a customer communication platform that uses an AI assistant powered by an Amazon Bedrock foundation model (FM). The AI assistant summarizes customer messages and generates initial response drafts.

The company wants to use Amazon Comprehend to implement layered content filtering. The layered content filtering must prevent sharing of offensive content, protect customer privacy, and detect potential inappropriate advice solicitation. Inappropriate advice solicitation includes requests for unethical practices, harmful activities, or manipulative behaviors.

The solution must maintain acceptable overall response times, so all pre-processing filters must finish before the content reaches the FM.

Which solution will meet these requirements?

Options:

A.  

Use parallel processing with asynchronous API calls. Use toxicity detection for offensive content. Use prompt safety classification for inappropriate advice solicitation. Use personally identifiable information (PII) detection without redaction.

B.  

Use custom classification to build an FM that detects offensive content and inappropriate advice solicitation. Apply personally identifiable information (PII) detection as a secondary filter only when messages pass the custom classifier.

C.  

Deploy a multi-stage process. Configure the process to use prompt safety classification first, then toxicity detection on safe prompts only, and finally personally identifiable information (PII) detection in streaming mode. Route flagged messages through Amazon EventBridge for human review.

D.  

Use toxicity detection with thresholds configured to 0.5 for all categories. Use parallel processing for both prompt safety classification and personally identifiable information (PII) detection with entity redaction. Apply Amazon CloudWatch alarms to filter metrics.

Discussion 0
Questions 4

A company has a recommendation system running on Amazon EC2 instances. The applications make API calls to Amazon Bedrock foundation models (FMs) to analyze customer behavior and generate personalized product recommendations.

The system experiences intermittent issues where some recommendations do not match customer preferences. The company needs an observability solution to monitor operational metrics and detect patterns of performance degradation compared to established baselines. The solution must generate alerts with correlation data within 10 minutes when FM behavior deviates from expected patterns.

Which solution will meet these requirements?

Options:

A.  

Configure Amazon CloudWatch Container Insights. Set up alarms for latency thresholds. Add custom token metrics using the CloudWatch embedded metric format.

B.  

Implement AWS X-Ray. Enable CloudWatch Logs Insights. Set up AWS CloudTrail and create dashboards in Amazon QuickSight.

C.  

Enable Amazon CloudWatch Application Insights. Create custom metrics for recommendation quality, token usage, and response latency using the CloudWatch embedded metric format with dimensions for request types and user segments. Configure CloudWatch anomaly detection on model metrics. Use CloudWatch Logs Insights for pattern analysis.

D.  

Use Amazon OpenSearch Service with the Observability plugin. Ingest metrics and logs through Amazon Kinesis and analyze behavior with custom queries.

Discussion 0
Questions 5

A pharmaceutical company is developing a Retrieval Augmented Generation (RAG) application that uses an Amazon Bedrock knowledge base. The knowledge base uses Amazon OpenSearch Service as a data source for more than 25 million scientific papers. Users report that the application produces inconsistent answers that cite irrelevant sections of papers when queries span methodology, results, and discussion sections of the papers.

The company needs to improve the knowledge base to preserve semantic context across related paragraphs on the scale of the entire corpus of data.

Which solution will meet these requirements?

Options:

A.  

Configure the knowledge base to use fixed-size chunking. Set a 300-token maximum chunk size and a 10% overlap between chunks. Use an appropriate Amazon Bedrock embedding model.

B.  

Configure the knowledge base to use hierarchical chunking. Use parent chunks that contain 1,000 tokens and child chunks that contain 200 tokens. Set a 50-token overlap between chunks.

C.  

Configure the knowledge base to use semantic chunking. Use a buffer size of 1 and a breakpoint percentile threshold of 85% to determine chunk boundaries based on content meaning.

D.  

Configure the knowledge base not to use chunking. Manually split each document into separate files before ingestion. Apply post-processing reranking during retrieval.

Discussion 0
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